Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization

Shih Hung Liu, Kuan Yu Chen, Yu Lun Hsieh, Berlin Chen, Hsin Min Wang, Hsu Chun Yen, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

摘要

Extractive summarization systems attempt to automatically pick out representative sentences from a source text or spoken document and concatenate them into a concise summary so as to help people grasp salient information effectively and efficiently. Recent advances in applying nonnegative matrix factorization (NMF) on various tasks including summarization motivate us to extend this line of research and provide the following contributions. First, we propose to employ graph-regularized nonnegative matrix factorization (GNMF), in which an affinity graph with its similarity measure tailored to the evaluation metric of summarization is constructed and in turn serves as a neighborhood preserving constraint of NMF, so as to better represent the semantic space of sentences in the document to be summarized. Second, we further consider sparsity and orthogonality constraints on NMF and GNMF for better selection of representative sentences to form a summary. Extensive experiments conducted on a Mandarin broadcast news speech dataset demonstrate the effectiveness of the proposed unsupervised summarization models, in relation to several widely-used state-of-the-art methods compared in the paper.

原文英語
主出版物標題2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9789881476821
DOIs
出版狀態已發佈 - 2017 一月 17
事件2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, 大韓民國
持續時間: 2016 十二月 132016 十二月 16

出版系列

名字2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

其他

其他2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
國家大韓民國
城市Jeju
期間16/12/1316/12/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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    Liu, S. H., Chen, K. Y., Hsieh, Y. L., Chen, B., Wang, H. M., Yen, H. C., & Hsu, W. L. (2017). Exploiting graph regularized nonnegative matrix factorization for extractive speech summarization. 於 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820883] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820883